Fritz Policelli

Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model

A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998‐2010) using a global retrospective simulation(3-hourlyand 1 /88spatialresolution)withtheTMPA3B42V6rainfall.Fourmethodswereexplored todefinefloodthresholds fromthemodelresults, includingthreepercentile-basedstatistical methodsandaLog Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability ofdetection (POD)] with longerflood durationsand largeraffected areas. The impactofdams wasdetectedinthevalidationstatistics,withthepresenceofdamstendingtoresultinmorefalse alarms and greater false-alarm duration. The GFMS validation statistics for flood durations .3 days and for areas without dams vary across the four methods, but center around a POD of ;0.70 and a false-alarm rate (FAR) of ;0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoffrouting model, taking into account the presence of dams, and improving the method for flood identification.

Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administrations Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world.

Application of satellite observations to manage natural disasters in the Lake Victoria Basin

Lake Victoria, the second largest fresh water lake in the Eastern part of Africa is a vital natural resource for the economic well being and prosperity of over 30 million people located in riparian regions of Uganda, Kenya and Tanzania. It covers a large area of about 68, 870 km2 and produces a GDP of about US

An integrated hydrological and water management study of the entire Nile river system - Lake Victoria to Nile delta

30 billion per year. The region is also very much prone to natural disasters such as severe floods during heavy precipitation periods in the Eastern part of Africa. In addition to floods, the precipitation also produces large infestations of mosquito larvae due to the standing water in many areas. This further causes multiple vector borne diseases such as Malaria, Rift Valley Fever and more. These problems are of serious concern and require active and aggressive surveillance and management to minimize the loss of human and animal lives and property damage. Satellite imagery and observations along with the in situ measurements provide a great tool to analyze and study this area and inform the policy makers to make calculated policy decisions which are beneficial to the environment. Recently, NASA and USAID have joined forces with the Regional Center for Mapping of Resources for Development (RCMRD) located in Nairobi, Kenya to utilize multiple NASA sensors such as TRMM, SRTM and MODIS to develop flood potential maps for the Lake Victoria Basin. The idea is to generate a flood forecasts and remote sensing data has proven extremely valuable for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the onset of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood detection b) flood forecasting, c) satellite data acquisition, d) rapid flood mapping and distribution, and e) validation of flood forecasting and detection products.

Improved hypoxia modeling for nutrient control decisions in the Gulf of Mexico

The Nile basin River system spans 3 million km2 distributed over ten nations. The eight upstream riparian nations, Ethiopia, Eretria, Uganda, Rwanda, Burundi, Congo, Tanzania and Kenya are the source of approximately 86% of the water inputs to the Nile, while the two downstream riparian countries Sudan and Egypt, presently rely on the rivers flow for most of the their needs. Both climate and agriculture contribute to the complicated nature of Nile River management: precipitation in the headwaters regions of Ethiopia and Lake Victoria is variable on a seasonal and inter-annual basis, while demand for irrigation water in the arid downstream region is consistently high. The Nile is, perhaps, one of the most difficult trans-boundary water issue in the world1, and this study would be the first initiative to combine NASA satellite observations with the hydrologic models study the overall water balance in a comprehensive manner. The cornerstone application of NASAs Earth Science Research Results under this project are the NASA Land Data Assimilation System (LDAS)2 and the USDA Atmosphere-Land Exchange Inverse (ALEXI)3 model. The end-users such as Regional Center for Mapping of Resources for Development (RCMRD, Nairobi, Kenya), Eastern Nile Technical Regional Office (ENTRO, Addis Ababa, Ethiopia), Ethiopian and Kenya Meteorological and Famine Early Warning System Network (FEWSNet) will be the eventual benefactors of this work.

Harnessing systems engineering methodology in using earth science research data for real applications

The Gulf of Mexico Modeling Framework is a suite of coupled models linking the deposition and transport of sediment and nutrients to subsequent bio-geo chemical processes and the resulting effect on concentrations of dissolved oxygen in the coastal waters of Louisiana and Texas. Here, we examine the potential benefits of using multiple NASA remote sensing data products within this Modeling Framework for increasing the accuracy of the models and their utility for nutrient control decisions in the Gulf of Mexico. Our approach is divided into three components: evaluation and improvement of (a) the precipitation input data (b) atmospheric constituent concentrations in EPAs air quality/deposition model and (c) the calculation of algal biomass, organic carbon and suspended solids within the water quality/eutrophication models of the framework.

Solar shield: forecasting and mitigating space weather effects on high-voltage power transmission systems

For the last three decades, Earth science remote sensing technologies have been providing an enormous amount of useful data and information serving to broaden our understanding of the home planet as a system. NASAs Earth science program has deployed about 18 complex satellites and is in the process of defining and launching multiple observing systems in this decade. At the same time, the European Community and many other countries such as Russia, France, India, Japan, and China have also significantly contributed to Earth science research. To date, the majority of such efforts have concentrated on expanding our scientific understanding of the multiple nonlinear and chaotic processes of Earths behavior. In recent years, legislators and stakeholders have put serious pressure on the science community to devote more attention to making use of scientific results for societal benefit. For instance, there are a number of areas such as energy forecasting, aviation safety, agricultural efficiency, disaster management, air quality and public health that can directly take advantage of Earth science results to analyze and predict large scale problems and conditions. This is becoming even more important now that we live in a global economy interconnected via the internet and transportation systems; regional environmental conditions can have far reaching impact across continental boundaries. These factors dictate requirements for global data that can help us assess and control the devastating problems of famine, water resources, wildfires, human health and more. To do this requires a serious, organized, and systematic approach that transfers fundamental research products to the applied sciences domain. This paper presents a systems engineering and management process that can effectively make such transfer of data to the user community. Examples are presented on how the above decision making framework can help in solving critical problems such as the spread of vector borne diseases, forecasts of harmful algal blooms as well as forest fires and wildfires, and the intercontinental transport of dust storms and pollution